Abstract
In this paper, a local distribution neural network is proposed for data clustering. This competing network is designed for non-stationary and evolving environment. It represents data by means of neurons (ellipsoids) arranged on a topology map. The local distribution is stored in ellipsoids, while the global topology information is preserving in the relationship between adjacent ellipsoids. With a self-adapting threshold strategy and iteratively learning for information of local distribution, the algorithm is operated in an incremental and on-line way. During implementation, The adopted metric is an improved Mahalanobis distance which considers the local distribution and implies the anisotropy on different vector basis. Hence it can be interpreted as an incremental version of Gaussian mixture model. Experiments both on artificial data and real-world data are carried out to show the performance of the proposed method.
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Ouyang, Q., Shen, F., Zhao, J. (2012). A Local Distribution Net for Data Clustering. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_37
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DOI: https://doi.org/10.1007/978-3-642-32695-0_37
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